{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e5684d15",
   "metadata": {},
   "source": [
    "# Poincare surface of section\n",
    "This example uses `rebound` to create a [Poincare surface of section](https://en.wikipedia.org/wiki/Poincaré_map) of the restricted circular three body problem (RC3BP). First, a series of RC3BP simulations with test particles at different semi-major axes are initialized at a fixed value of the [Jacobi constant](https://en.wikipedia.org/wiki/Jacobi_integral) $C_J$. Then, each simulation is integrated and the state of the test particle is recorded whenever the test particle and perturber are at opposition, i.e., $\\lambda - \\lambda_\\mathrm{p} = \\pi$ where $\\lambda$ and $\\lambda_\\mathrm{p}$ are the mean longitudes of the test particle and massive perturber, respectively. Finally, a surface of section showing the particles' periods versus mean anomalies is plotted. Numerous resonant islands are visible at period ratios corresponding to mean motion resonances between the particle and perturber.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "17909a74",
   "metadata": {},
   "outputs": [],
   "source": [
    "import rebound\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c1d218e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_sim(m_pert,n_pert,a_tp,l_pert,l_tp,e_tp,pomega_tp):\n",
    "    sim = rebound.Simulation()\n",
    "    sim.add(m=1)\n",
    "    P_pert = 2 * np.pi / n_pert\n",
    "    sim.add(m=m_pert,P=P_pert,l=l_pert)\n",
    "    sim.add(m=0.,a = a_tp,l=l_tp,e=e_tp,pomega=pomega_tp)\n",
    "    sim.move_to_com()\n",
    "    return sim"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc68afcd",
   "metadata": {},
   "source": [
    "Calculate the synodic angle, $\\psi = \\lambda - \\lambda_p$, at a specified time `T` from a simluation, `sim`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "75dca26a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_psi(T,sim):\n",
    "    ps = sim.particles\n",
    "    sim.integrate(T)\n",
    "    return np.mod(ps[1].l - ps[2].l ,2*np.pi)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "535243d5",
   "metadata": {},
   "source": [
    "Calculate the Jacobi constant of the test particle,\n",
    "$$\n",
    "C_J = n_p l_z - |\\pmb{v}|^2 -\\frac{Gm_*}{|\\pmb{r}-\\pmb{r_*}|}-\\frac{Gm_p}{|\\pmb{r}-\\pmb{r_p}|}\n",
    "$$\n",
    "where $l_z$ is the component of the test particle's specific angular momentum aligned with perturber's orbit normal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0dba8676",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_jacobi_const(sim):\n",
    "    ps = sim.particles\n",
    "    star = ps[0]\n",
    "    planet = ps[1]\n",
    "    particle = ps[2]\n",
    "    rstar = np.array(star.xyz)\n",
    "    rplanet = np.array(planet.xyz)\n",
    "    r = np.array(particle.xyz)\n",
    "    v = np.array(particle.vxyz)\n",
    "    \n",
    "    KE = 0.5 * v@v # test particle kinetic energy\n",
    "    mu1 = sim.G * star.m\n",
    "    mu2 = sim.G * planet.m\n",
    "    r1 = r-rstar\n",
    "    r2 = r-rplanet\n",
    "    PE = -1*mu1/np.sqrt(r1@r1) - mu2/np.sqrt(r2@r2) # test particle potential energy\n",
    "    \n",
    "    lz = np.cross(r,v)[-1]\n",
    "    \n",
    "    CJ = 2 * planet.n * lz - 2 * (KE + PE) # jacobi constant\n",
    "    return CJ\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc379bb0",
   "metadata": {},
   "source": [
    "# Run simulations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "506f21b2",
   "metadata": {},
   "source": [
    "Set the parameters of the simulations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b2bad65a",
   "metadata": {},
   "outputs": [],
   "source": [
    "m_pert = 3e-5\n",
    "n_pert = 5/4 * (1+0.05)\n",
    "e_tp = 0.0\n",
    "l_tp = 0\n",
    "l_pert = 0\n",
    "pomega_tp = 0.5 * np.pi"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7462fb61",
   "metadata": {},
   "source": [
    "Given a semi-major axis `a`, we solve for the eccentricity such that the Jacobi constant is equal to the user-specified value `CJ`. The eccentricity solution is assumed to lie in the interval specified by `e_bracket`. After finding a solution, we initialize and return a simulation with a test particle with the desired semi-major axis/eccentricity combination."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d204a48f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.optimize import root_scalar\n",
    "def get_sim_at_fixed_CJ(a,CJ,e_bracket):\n",
    "    get_sim_fn = lambda e,a: get_sim(m_pert,n_pert,a,l_pert,l_tp,e,pomega_tp)\n",
    "    root_fn = lambda e,a: get_jacobi_const(get_sim_fn(e,a)) - CJ\n",
    "    root = root_scalar(root_fn,args=(a,),bracket=e_bracket)\n",
    "    assert root.converged, \"Root-finding failed to converge for a={:.1f}, CJ={:.1f}\".format(a,CJ)\n",
    "    return get_sim_fn(root.root,a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7152b6e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_sos_data(sim,Npts,psi_section = np.pi):\n",
    "    ps = sim.particles\n",
    "    n_syn = ps[1].n - ps[2].n\n",
    "    Tsyn = 2 * np.pi / n_syn\n",
    "    n,e,M = np.zeros((3,Npts))\n",
    "    for i in range(Npts):\n",
    "        try:\n",
    "            rt=root_scalar(lambda t: get_psi(t,sim) - psi_section , bracket=[sim.t + 0.8*Tsyn,sim.t + 1.2*Tsyn])\n",
    "        except:\n",
    "            # re-compute Tsyn\n",
    "            n_syn = ps[1].n - ps[2].n\n",
    "            Tsyn = 2*np.pi/n_syn\n",
    "            rt=root_scalar(lambda t: get_psi(t,sim) - psi_section , bracket=[sim.t + 0.8*Tsyn,sim.t + 1.2*Tsyn])\n",
    "        n[i] = ps[2].n\n",
    "        e[i] = ps[2].e\n",
    "        M[i] = ps[2].M\n",
    "    return n,e,M"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "94e2fca1",
   "metadata": {},
   "outputs": [],
   "source": [
    "a_tp0 = 1\n",
    "sim0 = get_sim(m_pert,n_pert,a_tp0,l_pert,l_tp,e_tp,pomega_tp)\n",
    "CJ0 = get_jacobi_const(sim0)\n",
    "\n",
    "Nsims = 24 # Number of simulations to plot on surface of section\n",
    "Npts = 100 # Number of points to plot per simulation\n",
    "\n",
    "da_vals = np.linspace(0,0.1,Nsims)\n",
    "sims = [get_sim_at_fixed_CJ(a_tp0 + da,CJ0,[0,0.3]) for da in da_vals]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "22b19429",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rein/git/rebound/rebound/simulation.py:712: RuntimeWarning: At least 10 predictor corrector loops in IAS15 did not converge. This is typically an indication of the timestep being too large.\n",
      "  warnings.warn(msg[1:], RuntimeWarning)\n"
     ]
    }
   ],
   "source": [
    "all_pts = np.array([get_sos_data(sim,Npts) for sim in sims])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56394e50",
   "metadata": {},
   "source": [
    "The surface of section points are plotted below, along with the values of the test particles' eccentricities over the range of period ratio displayed in the surface of section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a1c57108",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(1,2,sharey=True,figsize=(12,5))\n",
    "\n",
    "min_pratio=0 \n",
    "max_pratio=np.inf\n",
    "for n,e,M in all_pts:\n",
    "    n_tp = n\n",
    "    alpha = (n/n_pert)**(2/3)\n",
    "    ecross = 1-alpha\n",
    "    ax[0].plot(M / np.pi, n/n_pert,'.')\n",
    "    ax[1].plot(e, n/n_pert,'.')\n",
    "\n",
    "# plot the orbit-crossing eccentricity for reference\n",
    "# versus period ratio\n",
    "pratios = np.linspace(*ax[0].get_ylim())\n",
    "alpha = pratios**(2/3)\n",
    "ecross=1-alpha\n",
    "ax[1].plot(ecross,pratios,'k-',lw=3,label=\"orbit crossing\")\n",
    "ax[1].legend()\n",
    "\n",
    "ax[0].set_ylabel(r\"$P_\\mathrm{pert}/P$\")\n",
    "ax[0].set_xlabel(r\"$M/\\pi$\")\n",
    "\n",
    "ax[1].set_xlabel(r\"$e$\")\n",
    "\n",
    "# plot the location of some resonances\n",
    "for a in ax:\n",
    "    a.axhline(3/4,ls='-',color='k',lw=2) # 1st order mmr\n",
    "    a.axhline(8/11,ls='-.',color='k') # 3rd order mmr\n",
    "    a.axhline(5/7,ls='--',color='k') # 2nd order mmr\n",
    "    a.axhline(7/10,ls='-.',color='k') # 3rd order mmr\n",
    "    a.axhline(2/3,ls='-',color='k') # first order mmr"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
